Displaying publications 461 - 480 of 616 in total

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  1. Mudali D, Jeevanandam J, Danquah MK
    Crit Rev Biotechnol, 2020 Nov;40(7):951-977.
    PMID: 32633615 DOI: 10.1080/07388551.2020.1789062
    Drug-induced transformations in disease characteristics at the cellular and molecular level offers the opportunity to predict and evaluate the efficacy of pharmaceutical ingredients whilst enabling the optimal design of new and improved drugs with enhanced pharmacokinetics and pharmacodynamics. Machine learning is a promising in-silico tool used to simulate cells with specific disease properties and to determine their response toward drug uptake. Differences in the properties of normal and infected cells, including biophysical, biochemical and physiological characteristics, plays a key role in developing fundamental cellular probing platforms for machine learning applications. Cellular features can be extracted periodically from both the drug treated, infected, and normal cells via image segmentations in order to probe dynamic differences in cell behavior. Cellular segmentation can be evaluated to reflect the levels of drug effect on a distinct cell or group of cells via probability scoring. This article provides an account for the use of machine learning methods to probe differences in the biophysical, biochemical and physiological characteristics of infected cells in response to pharmacokinetics uptake of drug ingredients for application in cancer, diabetes and neurodegenerative disease therapies.
    Matched MeSH terms: Computer Simulation
  2. Timmis J, Ismail AR, Bjerknes JD, Winfield AF
    Biosystems, 2016 Aug;146:60-76.
    PMID: 27178784 DOI: 10.1016/j.biosystems.2016.04.001
    Swarm robotics is concerned with the decentralised coordination of multiple robots having only limited communication and interaction abilities. Although fault tolerance and robustness to individual robot failures have often been used to justify the use of swarm robotic systems, recent studies have shown that swarm robotic systems are susceptible to certain types of failure. In this paper we propose an approach to self-healing swarm robotic systems and take inspiration from the process of granuloma formation, a process of containment and repair found in the immune system. We use a case study of a swarm performing team work where previous works have demonstrated that partially failed robots have the most detrimental effect on overall swarm behaviour. We have developed an immune inspired approach that permits the recovery from certain failure modes during operation of the swarm, overcoming issues that effect swarm behaviour associated with partially failed robots.
    Matched MeSH terms: Computer Simulation
  3. Farayola MF, Shafie S, Mohd Siam F, Khan I
    Comput Methods Programs Biomed, 2020 Apr;187:105202.
    PMID: 31835107 DOI: 10.1016/j.cmpb.2019.105202
    Background This paper presents a numerical simulation of normal and cancer cells' population dynamics during radiotherapy. The model used for the simulation was the improved cancer treatment model with radiotherapy. The model simulated the population changes during a fractionated cancer treatment process. The results gave the final populations of the cells, which provided the final volumes of the tumor and normal cells. Method The improved model was obtained by integrating the previous cancer treatment model with the Caputo fractional derivative. In addition, the cells' population decay due to radiation was accounted for by coupling the linear-quadratic model into the improved model. The simulation of the treatment process was done with numerical variables, numerical parameters, and radiation parameters. The numerical variables include the populations of the cells and the time of treatment. The numerical parameters were the model factors which included the proliferation rates of cells, competition coefficients of cells, and perturbation constant for normal cells. The radiation parameters were clinical data based on the treatment procedure. The numerical parameters were obtained from the previous literature while the numerical variables and radiation parameters, which were clinical data, were obtained from reported data of four cancer patients treated with radiotherapy. The four cancer patients had tumor volumes of 28.4 cm3, 18.8 cm3, 30.6 cm3, and 12.6 cm3 and were treated with different treatment plans and a fractionated dose of 1.8 Gy each. The initial populations of cells were obtained by using the tumor volumes. The computer simulations were done with MATLAB. Results The final volumes of the tumors, from the results of the simulations, were 5.67 cm3, 4.36 cm3, 5.74 cm3, and 6.15 cm3 while the normal cells' volumes were 28.17 cm3, 18.68 cm3, 30.34 cm3, and 12.54 cm3. The powers of the derivatives were 0.16774, 0.16557, 0.16835, and 0.16. A variance-based sensitivity analysis was done to corroborate the model with the clinical data. The result showed that the most sensitive factors were the power of the derivative and the cancer cells' proliferation rate. Conclusion The model provided information concerning the status of treatments and can also predict outcomes of other treatment plans.
    Matched MeSH terms: Computer Simulation
  4. Farayola MF, Shafie S, Siam FM, Khan I
    Comput Methods Programs Biomed, 2020 May;188:105306.
    PMID: 31901851 DOI: 10.1016/j.cmpb.2019.105306
    BACKGROUND: This paper presents a mathematical model that simulates a radiotherapy cancer treatment process. The model takes into consideration two important radiobiological factors, which are repair and repopulation of cells. The model was used to simulate the fractionated treatment process of six patients. The results gave the population changes in the cells and the final volumes of the normal and cancer cells.

    METHOD: The model was formulated by integrating the Caputo fractional derivative with the previous cancer treatment model. Thereafter, the linear-quadratic with the repopulation model was coupled into the model to account for the cells' population decay due to radiation. The treatment process was then simulated with numerical variables, numerical parameters, and radiation parameters. The numerical parameters which included the proliferation coefficients of the cells, competition coefficients of the cells, and the perturbation constant of the normal cells were obtained from previous literature. The radiation and numerical parameters were obtained from reported clinical data of six patients treated with radiotherapy. The patients had tumor volumes of 24.1cm3, 17.4cm3, 28.4cm3, 18.8cm3, 30.6cm3, and 12.6cm3 with fractionated doses of 2 Gy for the first two patients and 1.8 Gy for the other four. The initial tumor volumes were used to obtain initial populations of cells after which the treatment process was simulated in MATLAB. Subsequently, a global sensitivity analysis was done to corroborate the model with clinical data. Finally, 96 radiation protocols were simulated by using the biologically effective dose formula. These protocols were used to obtain a regression equation connecting the value of the Caputo fractional derivative with the fractionated dose.

    RESULTS: The final tumor volumes, from the results of the simulations, were 3.58cm3, 8.61cm3, 5.68cm3, 4.36cm3, 5.75cm3, and 6.12cm3, while those of the normal cells were 23.87cm3, 17.29cm3, 28.17cm3, 18.68cm3, 30.33cm3, and 12.55cm3. The sensitivity analysis showed that the most sensitive model factors were the value of the Caputo fractional derivative and the proliferation coefficient of the cancer cells. Lastly, the obtained regression equation accounted for 99.14% of the prediction.

    CONCLUSION: The model can simulate a cancer treatment process and predict the results of other radiation protocols.

    Matched MeSH terms: Computer Simulation
  5. Schilthuizen M, Craze PG, Cabanban AS, Davison A, Stone J, Gittenberger E, et al.
    J Evol Biol, 2007 Sep;20(5):1941-9.
    PMID: 17714311
    Although the vast majority of higher animals are fixed for one chiral morph or another, the cause for this directionality is known in only a few cases. In snails, for example, rare individuals of the opposite coil are unable to mate with individuals of normal coil, so directionality is maintained by frequency-dependent selection. The snail subgenus Amphidromus presents an unexplained exception, because dextral (D) and sinistral (S) individuals occur sympatrically in roughly equal proportions (so-called 'antisymmetry') in most species. Here we show that in Amphidromus there is sexual selection for dimorphism, rather than selection for monomorphism. We found that matings between D and S individuals occur more frequently than expected by chance. Anatomical investigations showed that the chirality of the spermatophore and the female reproductive tract probably allow a greater fecundity in such inter-chiral matings. Computer simulation confirms that under these circumstances, sustained dimorphism is the expected outcome.
    Matched MeSH terms: Computer Simulation
  6. Ang TK, Safuan HM, Sidhu HS, Jovanoski Z, Towers IN
    Bull Math Biol, 2019 07;81(7):2748-2767.
    PMID: 31201660 DOI: 10.1007/s11538-019-00627-8
    The present paper studies a predator-prey fishery model which incorporates the independent harvesting strategies and nonlinear impact of an anthropogenic toxicant. Both fish populations are harvested with different harvesting efforts, and the cases for the presence and non-presence of harvesting effort are discussed. The prey fish population is assumed to be infected by the toxicant directly which causes indirect infection to predator fish population through the feeding process. Each equilibrium of the proposed system is examined by analyzing the respective local stability properties. Dynamical behavior and bifurcations are studied with the assistance of threshold conditions influencing the persistence and extinction of both predator and prey. Bionomic equilibrium solutions for three possible cases are investigated with certain restrictions. Optimal harvesting policy is explored by utilizing the Pontryagin's Maximum Principle to optimize the profit while maintaining the sustainability of the marine ecosystem. Bifurcation analysis showed that the harvesting parameters are the key elements causing fishery extinction. Numerical simulations of bionomic and optimal equilibrium solutions showed that the presence of toxicant has a detrimental effect on the fish populations.
    Matched MeSH terms: Computer Simulation
  7. Packiam KAR, Ramanan RN, Ooi CW, Krishnaswamy L, Tey BT
    Appl Microbiol Biotechnol, 2020 Apr;104(8):3253-3266.
    PMID: 32076772 DOI: 10.1007/s00253-020-10454-w
    Over the past few decades, Escherichia coli (E. coli) remains the most favorable host among the microbial cell factories for the production of soluble recombinant proteins. Recombinant protein production (RPP) via E. coli is optimized at the level of gene expression (expression level) and the process condition of fermentation (process level). Presently, the reported studies do not give a clear view on the selection of methods employed in the optimization of RPP. Here, we have reviewed various optimization methods and their preferences with respect to the factors at expression and process levels to achieve the optimal levels of soluble RPP. With a greater understanding of these optimization methods, we proposed a stepwise methodology linking the factors from both levels for optimizing the production of soluble recombinant protein in E. coli. The proposed methodology is further explained through five sets of examples demonstrating the optimization of RPP at both expression and process levels.Key Points• Stepwise methodology of optimizing recombinant protein production is proposed.• In silico tools can facilitate the optimization of gene- and protein-based factors.• Optimization of gene- and protein-based factors aids host-vector selection.• Statistical optimization is preferred for achieving optimal levels of process factors.
    Matched MeSH terms: Computer Simulation
  8. Foo LS, Yap WS, Hum YC, Manan HA, Tee YK
    J Magn Reson, 2020 01;310:106648.
    PMID: 31760147 DOI: 10.1016/j.jmr.2019.106648
    Chemical exchange saturation transfer (CEST) magnetic resonance imaging (MRI) holds great potential to provide new metabolic information for clinical applications such as tumor, stroke and Parkinson's Disease diagnosis. Many active research and developments have been conducted to translate this emerging MRI technique for routine clinical applications. In general, there are two CEST quantification techniques: (i) model-free and (ii) model-based techniques. The reliability of these quantification techniques depends heavily on the experimental conditions and quality of the collected data. Errors such as noise may lead to misleading quantification results and thus inaccurate diagnosis when CEST imaging becomes a standard or routine imaging scan in the future. This paper investigates the accuracy and robustness of these quantification techniques under different signal-to-noise (SNR) levels and magnetic field strengths. The quantified CEST effect before and after adding random Gaussian White Noise using model-free and model-based quantification techniques were compared. It was found that the model-free technique consistently yielded larger average percentage error across all tested parameters compared to its model-based counterpart, and that the model-based technique could withstand SNR of about 3 times lower than the model-free technique. When applied on noisy brain tumor, ischemic stroke, and Parkinson's Disease clinical data, the model-free technique failed to produce significant differences between normal and abnormal tissue whereas the model-based technique consistently generated significant differences. Although the model-free technique was less accurate and robust, its simplicity and thus speed would still make it a good approximate when the SNR was high (>50) or when the CEST effect was large and well-defined. For more accurate CEST quantification, model-based techniques should be considered. When SNR was low (<50) and the CEST effect was small such as those acquired from clinical field strength scanners, which are generally 3T and below, model-based techniques should be considered over model-free counterpart to maintain an average percentage error of less than 44% even under very noisy condition as tested in this work.
    Matched MeSH terms: Computer Simulation
  9. Chua TH
    Trop Biomed, 2012 Mar;29(1):121-8.
    PMID: 22543612 MyJurnal
    According to the report of the Intergovernmental Panel on Climate Change (IPCC), Malaysia will experience an increase of 3-5°C in the future. As the development of the malaria parasite, Plasmodium falciparum, is sensitive to temperature, we investigated, using computer models, the effect of increase of 3º and 5ºC on the possible changes in the epidemiology of malaria transmission of P. falciparum in Malaysia. Four environmentally different locations were selected: Kuala Lumpur (KL), Cameron Highlands (CH), Kota Kinabalu (KK) and Kinabalu Park (KP). The extrinsic incubation period (EIP) was estimated using hourly temperatures and the mean daily temperatures. The EIP values estimated using the mean daily temperature were lower than those computed from hourly temperatures in warmer areas (KL, KK), but higher in the cooler areas (CH, KP). The computer simulations also indicated that the EIP will be decreased if the temperature was raised by 3º or 5ºC, with the effect more pronounced for the greater temperature increase, and for the cooler places. The vector cohort that is still alive at a time to transmit malaria (s(EIP)) also increased when the temperature was raised, with the increase more pronounced in the cooler areas. This study indicates an increase in temperature will have more significant effect in shortening the EIP in a cooler place (eg CH, KP), resulting in a greater s(EIP), and consequently increasing the transmission intensity and malaria risk. A temperature increase arising from the global climate change will likely affect the epidemiology of malaria in Malaysia, especially in the cooler areas.
    Matched MeSH terms: Computer Simulation
  10. Seraj F, Kanwal, Khan KM, Khan A, Ali M, Khalil R, et al.
    Mol Divers, 2021 Feb;25(1):143-157.
    PMID: 31965436 DOI: 10.1007/s11030-019-10032-x
    Novel ibuprofen derivatives 1-19 including ibuprofen hydrazide 1, and substituted thiourea derivatives 2-19 were synthesized and characterized by EI-MS, FAB-MS, HREI-MS, HRFAB-MS, 1H-, and 13C-NMR spectroscopic techniques. The synthetic molecules 1-19 were examined for their in vitro urease inhibition and were found to display a diversified degree of inhibitory potential in the range of IC50 = 2.96-178 μM as compared to the standard thiourea (IC50 = 21.32 ± 0.22 μM). Out of nineteen, thirteen derivatives 2-4, 6, 7, 9, 11-15, 17, and 18 demonstrated remarkable inhibitory activity with IC50 values of 2.96 ± 1.11 to 16.1 ± 1.07 μM, compound 5 exhibited moderate inhibition with IC50 value of 37.3 ± 0.41 μM, whereas, compounds 1, 8, and 10 demonstrated weak inhibition against urease enzyme. Almost all structural features are participating in the activity; however, limited structure-activity relationship was discussed on the basis of different structural features, i.e., different functional groups and their positions at aryl part. In addition, molecular docking study was performed in order to understand the ligands binding interactions with the active site of urease enzyme.
    Matched MeSH terms: Computer Simulation
  11. Laurent SJ, Werzner A, Excoffier L, Stephan W
    Mol Biol Evol, 2011 Jul;28(7):2041-51.
    PMID: 21300986 DOI: 10.1093/molbev/msr031
    Southeast Asian populations of the fruit fly Drosophila melanogaster differ from ancestral African and derived European populations by several morphological characteristics. It has been argued that this morphological differentiation could be the result of an early colonization of Southeast Asia that predated the migration of D. melanogaster to Europe after the last glacial period (around 10,000 years ago). To investigate the colonization process of Southeast Asia, we collected nucleotide polymorphism data for more than 200 X-linked fragments and 50 autosomal loci from a population of Malaysia. We analyzed this new single nucleotide polymorphism data set jointly with already existing data from an African and a European population by employing an Approximate Bayesian Computation approach. By contrasting different demographic models of these three populations, we do not find any evidence for an early divergence between the African and the Asian populations. Rather, we show that Asian and European populations of D. melanogaster share a non-African most recent common ancestor that existed about 2,500 years ago.
    Matched MeSH terms: Computer Simulation
  12. Sheikh Abdullah SN, Bohani FA, Nayef BH, Sahran S, Al Akash O, Iqbal Hussain R, et al.
    Comput Math Methods Med, 2016;2016:8603609.
    PMID: 27516807 DOI: 10.1155/2016/8603609
    Brain magnetic resonance imaging (MRI) classification into normal and abnormal is a critical and challenging task. Owing to that, several medical imaging classification techniques have been devised in which Learning Vector Quantization (LVQ) is amongst the potential. The main goal of this paper is to enhance the performance of LVQ technique in order to gain higher accuracy detection for brain tumor in MRIs. The classical way of selecting the winner code vector in LVQ is to measure the distance between the input vector and the codebook vectors using Euclidean distance function. In order to improve the winner selection technique, round off function is employed along with the Euclidean distance function. Moreover, in competitive learning classifiers, the fitting model is highly dependent on the class distribution. Therefore this paper proposed a multiresampling technique for which better class distribution can be achieved. This multiresampling is executed by using random selection via preclassification. The test data sample used are the brain tumor magnetic resonance images collected from Universiti Kebangsaan Malaysia Medical Center and UCI benchmark data sets. Comparative studies showed that the proposed methods with promising results are LVQ1, Multipass LVQ, Hierarchical LVQ, Multilayer Perceptron, and Radial Basis Function.
    Matched MeSH terms: Computer Simulation
  13. Acharya UR, Oh SL, Hagiwara Y, Tan JH, Adeli H, Subha DP
    Comput Methods Programs Biomed, 2018 Jul;161:103-113.
    PMID: 29852953 DOI: 10.1016/j.cmpb.2018.04.012
    In recent years, advanced neurocomputing and machine learning techniques have been used for Electroencephalogram (EEG)-based diagnosis of various neurological disorders. In this paper, a novel computer model is presented for EEG-based screening of depression using a deep neural network machine learning approach, known as Convolutional Neural Network (CNN). The proposed technique does not require a semi-manually-selected set of features to be fed into a classifier for classification. It learns automatically and adaptively from the input EEG signals to differentiate EEGs obtained from depressive and normal subjects. The model was tested using EEGs obtained from 15 normal and 15 depressed patients. The algorithm attained accuracies of 93.5% and 96.0% using EEG signals from the left and right hemisphere, respectively. It was discovered in this research that the EEG signals from the right hemisphere are more distinctive in depression than those from the left hemisphere. This discovery is consistent with recent research and revelation that the depression is associated with a hyperactive right hemisphere. An exciting extension of this research would be diagnosis of different stages and severity of depression and development of a Depression Severity Index (DSI).
    Matched MeSH terms: Computer Simulation
  14. Zakaria Z, Badhan RKS
    Eur J Pharm Sci, 2018 Jul 01;119:90-101.
    PMID: 29635009 DOI: 10.1016/j.ejps.2018.04.012
    Lumefantrine is a widely used antimalarial in children in sub-Saharan Africa and is predominantly metabolised by CYP3A4. The concomitant use of lumefantrine with the antiretroviral efavirenz, which is metabolised by CYP2B6 and is an inducer of CYP3A4, increases the risk of lumefantrine failure and can result in an increased recrudescence rate in HIV-infected children. This is further confounded by CYP2B6 being highly polymorphic resulting in a 2-3 fold higher efavirenz plasma concentration in polymorphic subjects, which enhances the potential for an efavirenz-lumefantrine drug-drug interaction (DDI). This study developed a population-based PBPK model capable of predicting the impact of efavirenz-mediated DDIs on lumefantrine pharmacokinetics in African paediatric population groups, which also considered the polymorphic nature of CYP2B6. The validated model demonstrated a significant difference in lumefantrine target day 7 concentrations (Cd7) in the presence and absence of efavirenz and confirmed the capability of efavirenz to initiate this DDI. This was more apparent in the *6/*6 compared to *1/*1 population group and resulted in a significantly lower (P 
    Matched MeSH terms: Computer Simulation
  15. TermehYousefi A, Tateno K, Bagheri S, Tanaka H
    Sci Rep, 2017 05 09;7(1):1623.
    PMID: 28487527 DOI: 10.1038/s41598-017-01855-5
    A method to fabricate a bioinspired nanobiosensor using electronic-based artificial taste receptors for glucose diagnosis is presented. Fabricated bioinspired glucose nanobiosensor designated based on an artificial taste bud including an amperometric glucose biosensor and taste bud-inspired circuits. In fact, the design of the taste bud-inspired circuits was inspired by the signal-processing mechanism of taste nerves which involves two layers. The first, known as a type II cell, detects the glucose by glucose oxidase and transduces the current signal obtained for the pulse pattern is conducted to the second layer, called type III cell, to induce synchronisation of the neural spiking activity. The oscillation results of fabricated bioinspired glucose nanobiosensor confirmed an increase in the frequency of the output pulse as a function of the glucose concentration. At high glucose concentrations, the bioinspired glucose nanobiosensor showed a pulse train of alternating short and long interpulse intervals. A computational analysis performed to validate the hypothesis, which was successfully reproduced the alternating behaviour of bioinspired glucose our nanobiosensor by increasing the output frequency and alternation of pulse intervals according to the reduction in the resistivity of the biosensor.
    Matched MeSH terms: Computer Simulation
  16. Al-Qaysi ZT, Zaidan BB, Zaidan AA, Suzani MS
    Comput Methods Programs Biomed, 2018 Oct;164:221-237.
    PMID: 29958722 DOI: 10.1016/j.cmpb.2018.06.012
    CONTEXT: Intelligent wheelchair technology has recently been utilised to address several mobility problems. Techniques based on brain-computer interface (BCI) are currently used to develop electric wheelchairs. Using human brain control in wheelchairs for people with disability has elicited widespread attention due to its flexibility.

    OBJECTIVE: This study aims to determine the background of recent studies on wheelchair control based on BCI for disability and map the literature survey into a coherent taxonomy. The study intends to identify the most important aspects in this emerging field as an impetus for using BCI for disability in electric-powered wheelchair (EPW) control, which remains a challenge. The study also attempts to provide recommendations for solving other existing limitations and challenges.

    METHODS: We systematically searched all articles about EPW control based on BCI for disability in three popular databases: ScienceDirect, IEEE and Web of Science. These databases contain numerous articles that considerably influenced this field and cover most of the relevant theoretical and technical issues.

    RESULTS: We selected 100 articles on the basis of our inclusion and exclusion criteria. A large set of articles (55) discussed on developing real-time wheelchair control systems based on BCI for disability signals. Another set of articles (25) focused on analysing BCI for disability signals for wheelchair control. The third set of articles (14) considered the simulation of wheelchair control based on BCI for disability signals. Four articles designed a framework for wheelchair control based on BCI for disability signals. Finally, one article reviewed concerns regarding wheelchair control based on BCI for disability signals.

    DISCUSSION: Since 2007, researchers have pursued the possibility of using BCI for disability in EPW control through different approaches. Regardless of type, articles have focused on addressing limitations that impede the full efficiency of BCI for disability and recommended solutions for these limitations.

    CONCLUSIONS: Studies on wheelchair control based on BCI for disability considerably influence society due to the large number of people with disability. Therefore, we aim to provide researchers and developers with a clear understanding of this platform and highlight the challenges and gaps in the current and future studies.

    Matched MeSH terms: Computer Simulation
  17. Sado F, Yap HJ, Ghazilla RAR, Ahmad N
    PLoS One, 2018;13(7):e0200193.
    PMID: 30001415 DOI: 10.1371/journal.pone.0200193
    Prolong walking is a notable risk factor for work-related lower-limb disorders (WRLLD) in industries such as agriculture, construction, service profession, healthcare and retail works. It is one of the common causes of lower limb fatigue or muscular exhaustion leading to poor balance and fall. Exoskeleton technology is seen as a modern strategy to assist worker's in these professions to minimize or eliminate the risk of WRLLDs. Exoskeleton has potentials to benefit workers in prolong walking (amongst others) by augmenting their strength, increasing their endurance, and minimizing high muscular activation, resulting in overall work efficiency and productivity. Controlling exoskeleton to achieve this purpose for able-bodied personnel without impeding their natural movement is, however, challenging. In this study, we propose a control strategy that integrates a Dual Unscented Kalman Filter (DUKF) for trajectory generation/prediction of the spatio-temporal features of human walking (i.e. joint position, and velocity, and acceleration) and an impedance cum supervisory controller to enable the exoskeleton to follow this trajectory to synchronize with the human walking. Experiment is conducted with four subjects carrying a load and walking at their normal speed- a typical scenario in industries. EMG signals taken at two muscles: Right Vastus Intermedius (on the thigh) and Right Gastrocnemius (on the calf) indicated reduction in muscular activation during the experiment. The results also show the ability of the control system to predict spatio-temporal features of the pilots' walking and to enable the exoskeleton to move in concert with the pilot.
    Matched MeSH terms: Computer Simulation
  18. Al-Dabbagh MM, Salim N, Himmat M, Ahmed A, Saeed F
    J Comput Aided Mol Des, 2017 Apr;31(4):365-378.
    PMID: 28220440 DOI: 10.1007/s10822-016-0003-4
    Chemical libraries contain thousands of compounds that need screening, which increases the need for computational methods that can rank or prioritize compounds. The tools of virtual screening are widely exploited to enhance the cost effectiveness of lead drug discovery programs by ranking chemical compounds databases in decreasing probability of biological activity based upon probability ranking principle (PRP). In this paper, we developed a novel ranking approach for molecular compounds inspired by quantum mechanics, called quantum probability ranking principle (QPRP). The QPRP ranking criteria would make an attempt to draw an analogy between the physical experiment and molecular structure ranking process for 2D fingerprints in ligand based virtual screening (LBVS). The development of QPRP criteria in LBVS has employed the concepts of quantum at three different levels, firstly at representation level, this model makes an effort to develop a new framework of molecular representation by connecting the molecular compounds with mathematical quantum space. Secondly, estimate the similarity between chemical libraries and references based on quantum-based similarity searching method. Finally, rank the molecules using QPRP approach. Simulated virtual screening experiments with MDL drug data report (MDDR) data sets showed that QPRP outperformed the classical ranking principle (PRP) for molecular chemical compounds.
    Matched MeSH terms: Computer Simulation
  19. Tung CH, Chen CW, Guo RC, Ng HF, Chu YW
    Biomed Res Int, 2016;2016:9480276.
    PMID: 27610389 DOI: 10.1155/2016/9480276
    Background. Quaternary structures of proteins are closely relevant to gene regulation, signal transduction, and many other biological functions of proteins. In the current study, a new method based on protein-conserved motif composition in block format for feature extraction is proposed, which is termed block composition. Results. The protein quaternary assembly states prediction system which combines blocks with functional domain composition, called QuaBingo, is constructed by three layers of classifiers that can categorize quaternary structural attributes of monomer, homooligomer, and heterooligomer. The building of the first layer classifier uses support vector machines (SVM) based on blocks and functional domains of proteins, and the second layer SVM was utilized to process the outputs of the first layer. Finally, the result is determined by the Random Forest of the third layer. We compared the effectiveness of the combination of block composition, functional domain composition, and pseudoamino acid composition of the model. In the 11 kinds of functional protein families, QuaBingo is 23% of Matthews Correlation Coefficient (MCC) higher than the existing prediction system. The results also revealed the biological characterization of the top five block compositions. Conclusions. QuaBingo provides better predictive ability for predicting the quaternary structural attributes of proteins.
    Matched MeSH terms: Computer Simulation
  20. Leong CO, Lim E, Tan LK, Abdul Aziz YF, Sridhar GS, Socrates D, et al.
    Magn Reson Med, 2019 02;81(2):1385-1398.
    PMID: 30230606 DOI: 10.1002/mrm.27486
    PURPOSE: To evaluate a 2D-4D registration-cum-segmentation framework for the delineation of left ventricle (LV) in late gadolinium enhanced (LGE) MRI and for the localization of infarcts in patient-specific 3D LV models.

    METHODS: A 3-step framework was proposed, consisting of: (1) 3D LV model reconstruction from motion-corrected 4D cine-MRI; (2) Registration of 2D LGE-MRI with 4D cine-MRI; (3) LV contour extraction from the intersection of LGE slices with the LV model. The framework was evaluated against cardiac MRI data from 27 patients scanned within 6 months after acute myocardial infarction. We compared the use of local Pearson's correlation (LPC) and normalized mutual information (NMI) as similarity measures for the registration. The use of 2 and 6 long-axis (LA) cine-MRI scans was also compared. The accuracy of the framework was evaluated using manual segmentation, and the interobserver variability of the scar volume derived from the segmented LV was determined using Bland-Altman analysis.

    RESULTS: LPC outperformed NMI as a similarity measure for the proposed framework using 6 LA scans, with Hausdorrf distance (HD) of 1.19 ± 0.53 mm versus 1.51 ± 2.01 mm (endocardial) and 1.21 ± 0.48 mm versus 1.46 ± 1.78 mm (epicardial), respectively. Segmentation using 2 LA scans was comparable to 6 LA scans with a HD of 1.23 ± 0.70 mm (endocardial) and 1.25 ± 0.74 mm (epicardial). The framework yielded a lower interobserver variability in scar volumes compared with manual segmentation.

    CONCLUSION: The framework showed high accuracy and robustness in delineating LV in LGE-MRI and allowed for bidirectional mapping of information between LGE- and cine-MRI scans, crucial in personalized model studies for treatment planning.

    Matched MeSH terms: Computer Simulation
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